ETL

Index:

1. Introduction to ETL:

• Overview of ETL concepts, components, and workflow.
• Understanding the role of ETL in data integration and warehousing.
• Introduction to data sources, destinations, and transformation rules.

2. Data Extraction (E):

• Extracting data from various sources, including databases, files, APIs, and streaming sources.
• Understanding data extraction methods, such as full load, incremental load, and change data capture (CDC).
• Implementing data extraction techniques for structured, semi-structured, and unstructured data.

3. Data Transformation (T):

• Performing data transformation tasks such as cleansing, normalization, aggregation, and enrichment.
• Exploring common data transformation techniques and algorithms.
• Implementing business rules, calculations, and data quality checks in ETL workflows.

4. Data Loading (L):

• Loading transformed data into target systems, including data warehouses, data marts, and operational databases.
• Understanding different loading strategies, such as bulk loading, batch loading, and real-time loading.
• Implementing error handling, logging, and auditing in data loading processes.

5. ETL Tools and Technologies:

• Overview of popular ETL tools and platforms, such as Informatica, Talend, and Apache NiFi.
• Exploring ETL features and capabilities for data integration, transformation, and loading.
• Comparing and evaluating ETL tools based on performance, scalability, and cost.

6. ETL Performance Optimization:

• Optimizing ETL processes for performance, scalability, and efficiency.
• Implementing parallel processing, partitioning, and caching techniques in ETL workflows.
• Monitoring and tuning ETL jobs for resource utilization and throughput.

7. Error Handling and Monitoring:

• Implementing error handling mechanisms in ETL workflows for data quality and consistency.
• Monitoring ETL jobs and workflows for performance, errors, and exceptions.
• Implementing logging, alerting, and notification mechanisms in ETL processes.

8. ETL Best Practices and Patterns:

• Understanding best practices for ETL design, development, and maintenance.
• Implementing ETL design patterns such as star schema, snowflake schema, and slowly changing dimensions (SCDs).
• Addressing common challenges and pitfalls in ETL projects.